docstr: ConvTasNet model works on the valid configurations in the paper
docstr_tok: ["ConvTasNet","model","works","on","the","valid","configurations","in","the","paper"]
code: def test_paper_configuration(self, num_sources, model_params):
      """ConvTasNet model works on the valid configurations in the paper"""
      batch_size = 32
      num_frames = 8000
  
      model = ConvTasNet(
          num_sources=num_sources,
          enc_kernel_size=model_params.enc_kernel_size,
          enc_num_feats=model_params.enc_num_feats,
          msk_kernel_size=model_params.msk_kernel_size,
          msk_num_feats=model_params.msk_num_feats,
          msk_num_hidden_feats=model_params.msk_num_hidden_feats,
          msk_num_layers=model_params.msk_num_layers,
          msk_num_stacks=model_params.msk_num_stacks,
      )
      tensor = torch.rand(batch_size, 1, num_frames)
      output = model(tensor)
  
      assert output.shape == (batch_size, num_sources, num_frames)
  
imports: import itertools
  from collections import namedtuple
  import torch
  from parameterized import parameterized
  from torchaudio.models import ConvTasNet, DeepSpeech, Wav2Letter, WaveRNN
  from torchaudio.models.wavernn import MelResNet, UpsampleNetwork
  from torchaudio_unittest import common_utils
  from torchaudio_unittest.common_utils import torch_script
label_desc: Use image node_id 1 for calling the TestConvTasNet obj's underlying member method code with example usage: obj.test_paper_configuration(num_sources, model_params) without return types